import pandas as pd
from statsforecast.distributed.utils import forecast
from statsforecast.distributed.fugue import FugueBackend
from statsforecast.models import AutoARIMA

df = pd.read_parquet(f"{WORKING_DIR}/combined.parquet")[["unique_id", "ds", "y","sell_price"]]
result = forecast(df, 
                  models=[AutoARIMA(season_length=7)], 
                  freq="D", 
                  h=7,
                  parallel=FugueBackend("dask"))
result.compute()

from statsforecast import StatsForecast

df = pd.read_parquet(f"{WORKING_DIR}/combined.parquet")
df = df[["unique_id", "ds", "y","sell_price"]]

model = StatsForecast(
    df=df,
    models=[AutoARIMA(season_length=7)], 
    freq='D', 
    n_jobs=-1
)
model.forecast(7)